Method for superimposing statistical information on tabular data

ABSTRACT

A method is disclosed for displaying a plurality of statistical data usually presented in a histogram, such as sample counts and percentages of a collection of categorized samples, in a compact single table. The method comprises presenting grouped statistical data that exists within a collection of “buckets” and presenting the sample count for the collected data as an integer in a corresponding cell in the table. Additionally, as disclosed by the present invention, the percentage value of the samples located in each bucket data cell is represented in the data cell as a superimposed gray-scale representation. Presenting the percentages in gray-scale provides overall clarity to the table, assists in ensuring that data can be quickly and easily interpreted and not be subject to misinterpretation, and further allows for the compact display of such information in a single table and subsequent manipulation by automated analysis tools.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. ProvisionalApplication No. 60/465,045, filed Apr. 24, 2003.

FIELD OF THE INVENTION

[0002] The present invention disclosed herein relates to the displayingof any statistical data and particularly to a method for superimposing agraph onto tabular data that is normally displayed utilizing a barchart. Even more particularly, the invention relates to statistical datacontaining sample counts and percentages, wherein such data is displayedwithin a single table.

BACKGROUND OF THE INVENTION

[0003] Statistics have been used for centuries to quantify data. Todayspecific statistical measures and characteristics of database and schemaobjects and other forms of data presentation, such as the datadistribution and storage characteristics of tables, columns, indexes,and partitions, are valuable to users and analysts and can be presentedin a plurality of forms. One such example is as a histogram. In viewinginformation and characterizing a set of samples, a histogram can providea more complete picture of the distribution of the data than statisticalmeasures such as the mean and standard deviation, etc. This is done bypartitioning the data into a collection of buckets and reporting thenumber or percentage of samples that fall into each bucket. This reportcan take on various forms. Commonly used forms include tables, line,bar, and pie charts.

[0004] The histogram has become a popular tool used in graphing datafrom databases and other data sources. The histogram is used tosummarize discrete or continuous data that are measured on an intervalscale. In a line or bar chart presentation of a histogram, anindependent variable (usually a bucket or range of data) is plottedalong the horizontal axis of the histogram, and the dependent variable(usually a percentage) is plotted along the vertical axis of thehistogram. The independent variable is capable of attaining only afinite number of discrete values (for example, five) rather than acontinuous range of values. However, the dependent variable can span acontinuous range.

[0005] Histograms are also often used to illustrate the major featuresof the distribution of data in a convenient form. A histogram divides upthe range of possible values in a data set into classes, groups, orbuckets. In a bar chart histogram, for each class, group, or bucket arectangle is constructed with a base lengths being equal and the heightproportional to the number of observations falling into that class,group, or bucket.

[0006] Generally, a bar chart histogram will have bars of equal width,although this is not the case when class, group, or bucket intervalsvary in size. The intervals do not have to be equal. For example, onebucket could be 0-5 while a second bucket is 6-15. Histograms can havean appearance similar to a vertical or horizontal bar graph. When thevariables are continuous (i.e., a variable which can assume an infinitenumber of real values . . . e.g., an individual can walk 2.456721 . . .miles) there no gaps are present between the bars. However, when thevariables are discrete (i.e., a numeric value that takes only a finitenumber of real values . . . e.g., X can equal only 1, 3, 5, and 1,000)gaps should be left between the bars. In general, Graph 1 below providesa good example of a histogram.

[0007] To analysts, the strength of a histogram is that it provides aneasy-to-read picture of the location and variation within a data set.There are, however, various weaknesses in histograms. The first is thathistograms can be manipulated to show different pictures. In suchmanipulations if too few or too many bars are used, the histogram can bevery misleading. This is an area which requires some judgment, andperhaps various levels of experimentation, all based on the analyst'sexperience.

[0008] Another weakness is that histograms can also obscure differencesamong data sets. For example, if you looked at data for the number ofbirths per day in the United States in 2003, you would miss any certainvariations (e.g. births to single parents, born as twins, mortalityinformation etc.). Likewise, in industry applications, a histogram of aparticular process run can usually tell only one part of a long story.There then evolves a need to keep reviewing the histograms and controlcharts for consecutive similar process runs over an extended time togain useful knowledge about the specific process.

[0009] The analysis of the shape or the clustering of statistical datawithin histograms also lends useful information to analysts. Clustering,in one definition, deals with finding a structure in a collection ofunlabeled data. Clustering could also be further defined as the processof organizing objects into groups whose members are similar in some way.A cluster is, therefore, a collection of objects which are “similar”between them and are “dissimilar” to the objects belonging to otherclusters. So, the goal of clustering is to determine the intrinsicgrouping in a set of unlabeled data.

[0010] Cluster analysis is data analysis with an objective of sortingcategories or cases (people, things, events, etc) into groups, orclusters, so that the degree of association is strong between members ofthe same cluster and weak between members of different clusters. Eachcluster thus describes, in terms of the data collected, the class towhich its members belong; and this description may be abstracted throughuse from the particular to the general class or type.

[0011] Frequency information, as it relates to statistical data, is alsoan important analysis tool. The frequency of a particular observation isdefined as the number of times the observation occurs in the data. Thedistribution of a variable is the pattern of frequencies of theobservation. Frequency distributions can be portrayed as frequencytables, histograms, or polygons. Frequency distributions can show eitherthe actual number of observations falling in each range or thepercentage of observations. In the latter instance, the distribution iscalled a relative frequency distribution.

[0012] Frequency distribution tables can be used for both categoricaland numeric variables. Numeric variables may be either continuous ordiscrete.

[0013] A continuous variable is said to be continuous if it can assumean infinite number of real values. Examples of a continuous variable aredistance, age and temperature. Continuous variables should only be usedwith class intervals, which will be explained below. The measurement ofa continuous variable is restricted by the methods used, or by theaccuracy of the measuring instruments. For example, the height of astudent is a continuous variable because a student may be 5.5321748755 .. . feet tall. However, when the height of a person is measured, it isusually measured to the nearest half inch. Thus, this student's heightwould be recorded as 5½ feet.

[0014] Discrete variables can only take a finite number of real values.An example of a discrete variable would be the score given by a judge toa gymnast in competition: the range is 0 to 10 and the score is alwaysgiven to one decimal (e.g., a score of 8.5). Discrete variables may alsobe grouped. Again, grouping variables makes them easier to handle.

[0015] What follows below is an explanation of constructing a series ofdifferent types of frequency distribution tables. Each example is shownto depict the various, but unlimited, types of data that is compiled foruse in histograms.

EXAMPLE 1 Constructing a Frequency Distribution Table

[0016] A survey was taken on Bridle Path Street and in each of the 20homes, families were asked how many children live in their household.The results of the survey were recorded as follows:

[0017] 1, 2, 1, 0, 3, 4, 0, 1, 1, 1, 2, 2, 3, 2, 3, 2, 1, 4, 0, 0.

[0018] The following steps can be used to present this data in afrequency distribution table.

[0019] 1. Divide the results (x) into intervals, and then count thenumber of results in each interval. In this case, the intervals would bethe number of households with no children (0), one child (1), twochildren (2) and so forth.

[0020] 2. Make a table with separate columns for the interval numbers(the number of children per household), the tallied results, and thefrequency of results in each interval. Label these columns Number ofchildren, Tally and Frequency.

[0021] 3. Read the list of data from left to right and place a tallymark in the appropriate row. For example, the first result is a 1, soplace a tally mark in the row beside where 1 appears in the intervalcolumn (Number of children). The next result is a 2, so place a tallymark in the row beside the 2, and so on.

[0022] 4. Add up the number of tally marks in each row and record themin the final column entitled Frequency.

[0023] An example of a frequency distribution table for the exampleabove would be similar to the Table 1 below. By looking at thisfrequency distribution in Table 1 we can see that out of 20 householdssurveyed, 4 households had no children, 6 households had 1 child, etc.TABLE 1 Frequency table for the number of children in each householdNumber of children (x) Tally Frequency (f) 0 1111 4 1 111111 6 2 11111 53 111 3 4 11 2

[0024] If a variable takes a large number of values, then it is easierto present and handle the data by grouping the values into what is knownas class intervals. As mentioned above, continuous variables are morelikely to be presented in class intervals, while discrete variables canbe grouped into class intervals or not. To illustrate, suppose one setsout age ranges for a study of young people, while allowing for thepossibility that some older people may also fall into the scope of ourstudy.

[0025] The frequency of a class interval is the number of observationsthat occur in a particular predefined interval. So, for example, if 20people aged 5 to 9 appear in our study's data, the frequency for the 5-9interval is 20.

[0026] The endpoints of a class interval are the lowest and highestvalues that a variable can take. So, the intervals in our study are 0 to4 years, 5 to 9 years, 10 to 14 years, 15 to 19 years, 20 to 24 years,and 25 years and over. The endpoints of the first interval are 0 and 4if the variable is discrete, and 0 and 4.999 if the variable iscontinuous. The endpoints of the other class intervals would bedetermined in the same way.

EXAMPLE 2 Constructing Frequency Distribution Tables for Large Numbersof Observations

[0027] In this example thirty AA batteries were tested to determine howlong they would last. The results, to the nearest minute of duration,were recorded as follows: 423, 369, 387, 411, 393, 394, 371, 377, 389,409, 392, 408, 431, 401, 363, 391, 405, 382, 400, 381, 399, 415, 428,422, 396, 372, 410, 419, 386, and 390.

[0028] Using the steps in Example 1, the given data, and a classinterval of 10, a frequency distribution table can be constructedwherein the interval for the first class is 360 to 369 and includes 363(the lowest value). The completed frequency distribution table wouldlook similar to Table 2 below. TABLE 2 Life of AA batteries, in minutesBattery life, minutes (x) Tally Frequency (f) 360-369 11 2 370-379 111 3380-389 11111 5 390-399 1111111 7 400-409 11111 5 410-419 1111 4 420-429111 3 430-439 1 1 Total 30

[0029] Today, problems exist when using histograms containing the typesof information compiled and using data as in the previous examples.These problems specifically relate on how to display any tabular datacontained therein into a compact, discernable, and easily interpretedform that is suitable for additional automated analysis. This problem isexacerbated in the case where the samples are categorized. In suchcases, it is often desirable to display a histogram for each categoryand one for the composite. Therefore, what is now needed is a method fororganizing and compactly presenting a collection of categorized sampleswhile ensuring that the tabular data contained therein is easily readand interpreted by both the user/analyst and the various analysis tools,such as spreadsheets.

SUMMARY OF THE INVENTION

[0030] It is therefore the primary object of the present invention toprovide a method for organizing and compactly displaying a collection ofcategorized data samples that simultaneously allows for ease ofinterpretation by the user or analyst and further automated analysis ofthe tabular data contained therein.

[0031] In this invention, data is organized within a single tablebecause it offers both a compact representation of the data and can beeasily interpreted by automated analysis tools such as spreadsheets. Forexample, to aid the user/analyst in interpreting percentage data withincertain data cells, all cells containing percentage data are graphicallysuperimposed with various shades of gray to indicate the relativepercentage of samples contained in the data cell within each bucket.This provides the reader with a graphical presentation of the datadistribution without requiring additional tabular space.

[0032] The present invention provides columns which identify thecategory, count, and buckets for the data contained. There is one rowfor each category and one row for the composite. The composite row canbe either the first or last row. The sample count for each bucket ispresented as an integer in the corresponding cell. The percentage ofsamples in each bucket is represented as a level of gray. While it ispossible to present both the sample count and percentage as numericvalues in a single cell, e.g., “312 (28%)” such an approach makes itmore difficult for the reader to quickly interpret the data and greatlycomplicates further automated analysis.

[0033] Presenting the percentages in gray scale provides a picture ofthe data that can be quickly interpreted. Using gray scale depiction forpercentage information allows the data to be interpreted withoutrequiring a key. Furthermore, it is not subject to misinterpretation dueto a user's physical limitations (e.g., color blindness). However, bysuperimposing gray scale of the cell data is not meant to provide ananalyst with exact percentage information but rather a generalized quickreference. Combining the histogram data with the standard statisticmeasures provides a complete compact view of the data, which is easy togenerate and can be viewed by HTML browsers, and also viewed andmanipulated in spreadsheets.

[0034] Other methods, systems, features, and advantages of the presentinvention will be or become apparent to one with skill in the art uponexamination of the drawings and detailed description. It is intendedthat all such additional methods, systems, features, and advantages beincluded within this description, be within the scope of the presentinvention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Many of the aspects of the invention can be better understoodwith reference to the following drawings. The components in the drawingsare not necessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present invention. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

[0036]FIG. 1 is a table depicting a “before” example of a non-compactstatistical table having a plurality of statistical data representedwith an associated bar graph depiction;

[0037]FIG. 2 is a table depicting an “after” example of a compact tabledisplay of the statistical information shown in FIG. 1 using the methodof the present invention;

[0038]FIG. 3 is a flow block diagram depicting the method according tothe present invention; and,

[0039]FIG. 4 illustrates an example of the algorithm used with themethod according to the present invention.

DETAILED DESCRIPTION

[0040] In the descriptions that follow, like parts are marked throughoutthe specification and drawings with the same numerals, respectively. Thedrawing figures are not necessarily drawn to scale and certain figuresmay be shown in exaggerated or generalized form in the interest ofclarity and conciseness. The invention is described with reference tospecific embodiments. It will, however, be evident that variousmodifications and changes may be made thereto without departing from thebroader spirit and scope of the invention.

[0041] The reader is to understand that the specific ordering andcombination of method actions shown in the method flow diagramsdescribed herein are merely illustrative, and the invention can beperformed using different, additional, or differentcombinations/ordering of method actions and components. For example, theinvention is particularly illustrated herein with reference to specificdatabase objects such as tables, columns, and rows, but it is noted thatthe inventive principles are equally applicable to other types and formsof data presentation as well. The specification and drawings are,accordingly, to be regarded in an illustrative rather than restrictivesense.

[0042] The present invention disclosed herein is found within a softwareprogram that enables a user or analyst to construct a compact singletable for quickly identifying and interpreting various statistical dataand information presented therein. Such data includes, but is notlimited to, sample counts, gray-scale depiction of percentageinformation, categories, bucket ranges, and other standard statisticalmeasures.

[0043] The present invention makes it easier to identify the shape orclustering of the various statistical data collected and presented bylooking at a gray scale depiction for frequency or percentageinformation. The general shape or clustering of data can be determinedby referring to the single table disclosed herein and by specificallyviewing a gray scale depiction of the percentages or distribution. Theeffectiveness of the present invention is realized by the application ofthe method disclosed herein to examples 1 and 2 above and, below, inExample 3 as relating to the construction of relative frequency andpercentage frequency tables.

[0044] For example, an analyst studying relative frequency andpercentage frequency data might want to know not only how long batterieslast as depicted in Table 2 of Example 2, but also might want to knowwhat proportion of the batteries falls into each class interval ofbattery life. The relative frequency of a particular observation orclass interval is found by dividing the frequency (f) by the number ofobservations (n): that is, (f÷n). Thus relativefrequency=frequency÷number of observations. The percentage frequency isthen found by multiplying each relative frequency value by 100. ThusPercentage frequency=relative frequency×100=f÷n×100.

[0045] Although the intent of the present invention is not to facilitatethe extraction of exact frequency data nor is it practical for auser/analyst to attempt to glean the exact percentages, it is intendedto provide a quick-look general indication of frequency distributionswhich are encoded by the present invention in gray-scale. This becomesspecifically advantageous where the data is clustered. In example 2,Table 2, the gray-scale method of the present invention would depict thepercentage of batteries falling into each specific class interval ofbattery life. If desired, frequency data can also be included asadditional columns in the table. However, such columns should not beinterspersed with the sample count columns, since that would complicatesubsequent automated analysis.

EXAMPLE 3 Constructing Relative Frequency and Percentage FrequencyTables

[0046] Using the data from Example 2 above, a frequency table can beconstructed giving the relative frequency and percentage frequency ofeach interval of battery life the table would appear similar as that inTable 3. TABLE 3 Life of AA batteries, in minutes Battery life,Frequency Relative Percent minutes (x) (f) frequency frequency 360-369 20.07 7 370-379 3 0.10 10 380-389 5 0.17 17 390-399 7 0.23 23 400-409 50.17 17 410-419 4 0.13 13 420-429 3 0.10 10 430-439 1 0.03 3 Total 301.00 100

[0047] An user/analyst reviewing and utilizing these data could now saythat:

[0048] 7% of AA batteries have a life of from 360 minutes up to but lessthan 370 minutes, and that the probability of any randomly selected AAbattery having a life in this range is approximately 0.07.

[0049] After data has been collected, the data as presented in Table 3above can be used by the method of the present invention to compactlyand simultaneously display all information using only one tablecomprising a column header and a single data row. For example, the firstcolumn in Table 3 above “Battery life, minutes (x)” would be convertedto column headers. The second column of Table 3 “Frequency (f)” would bepresented as numeric values in the data row. The third and fourthcolumns of Table 3, “Relative frequency” and “Percent frequency” (whichare identical except for scale), would be presented as gray-scalebackground in the data cell for the data row.

[0050] Now referring to FIG. 1, the data shown within the table in FIG.1 contains various types of statistical data, most of which includefrequency distribution data. The data shown specifically relates toresponse statistics between two nodes (e.g., biu.enter_biu=>leave_biu)and represents the time it takes for specific data to travel between thenodes indicated. The data within FIG. 1 is shown as an example of typesof data and is not intended to limit the scope of types of statisticaldata that can be used by the present invention to display as disclosedherein. Additionally, the method of the present invention is notintended to be limited to the display of histogram data, but is alsoapplicable to any tabular data that would normally be displayed using abar chart.

[0051] In FIG. 1 the data presented therein specifically relates to thedata's context 10, category 115, minimum 15, maximum 25 and mean 20response times, standard deviation 30, coefficient of variation 105, andsamples of data 40 under each bucket range 35. The columns 35 displayedto the right of the coefficient of variation (“CoV”) column 105 (i.e.,(−∞, 2], (2, 4], (4, 6], (6, 8], (8, 10], (10, 12], (12, 14], (14, 16],(16, 18], (18, ∞] contain the various distribution data within thestated bucket's interval range. Specifically, FIG. 1 shows a “before”table with a plurality of data being displayed in a non-compact form andan associated bar graph of the data before the method of the presentinvention was applied to alter its display characteristics.

[0052] In FIG. 1 under the sample counts dread category 45, it is showntherein that 261 response time values were between (2,4], 122 werebetween (4,6], 216 were between (6,8], 199 were between (8,10] . . .etc. Another way of representing frequency distribution is withpercentages. For example, in FIG. 1, 5 percent of the response timeswere between 2 and 4 seconds, 2 percent were between 4 and 6 seconds, 4percent were between 6 and 8 seconds and 4 percent were between 8 and 10seconds . . . etc. It is often important and valuable to know both theabsolute counts as well as the percentages, but displaying both forms ofdata information within one table, as shown here in FIG. 1, or twotables can be awkward, difficult to follow, and harder to read thanother more compact and simpler tables.

[0053] In FIG. 1, a user/analyst is forced to inconveniently referbetween the sample count portion 107 and the accompanying bar graph 106to ascertain detailed information such as a category's 115 sample count,percentage information, and other useful data.

[0054] However, with software programmed with the method of the presentinvention the percentage information can be easily calculated andsuperimposed, as shown in FIG. 2, over the absolute values in relativedata cells using shades of gray 110 determined, assigned, and displayedby the method presented herein. More specifically, cell data rangeshaving higher/larger percentage values are displayed by the presentinvention in darker shades of gray. The shade of gray gets incrementallydarker as the percentage value increases. In contrast, cell data rangeshaving lower/smaller percentage values are displayed by the presentinvention in lighter shades of gray. The shades of gray incrementallyget lighter as the relative percentage value decreases. The effect ofusing percentage value gray shading quickly draws the reader's attentionto the ranges with the highest/largest percentages and gives an overall“quick-look picture” of the frequency distribution, thereby eliminatingthe inconveniences noted above when viewing large tables or two tablesat a time.

[0055] As graphically displayed in FIG. 2, the method of the presentinvention is realized by providing a novel technique that enables theuser/analyst to display both sample counts and percentages of acollection of categorized samples within a single compact single table.It should be understood that the applicability of the method disclosedherein is universal anytime statistical data is desired to be presentedin a single compact tabular form. The following are examples of varioustypes of information wherein samples can be taken and used with thepresent invention: the arrival of work at a particular node such as aserver or an electronic gate (i.e., a request for service of any generalsense), and arrival rates of “work” at a server (i.e., the number oftransactions waiting within a queue). These examples are not meant to belimiting or exhaustive in type or amount.

[0056] In FIG. 2 an example table is depicted of the preferredembodiment of present invention. Specifically referring to FIG. 2, thefirst column identifies the “Context” 100 (e.g.,biu.enter_biu=>leave_biu) and the second column identifies the“Category” 115 of data (e.g., dread, dwrite, iread). The third, fourth,and fifth columns identify the minimum, mean, and maximum response time,respectively. Next, column 6 identifies the “Count” (i.e., the number ofsamples) total 117 information for each row (120, 125, and 130),including the composite count 140 for all rows. Following the Countcolumn 117 are the standard deviation and coefficient of variationcolumns, respectively. In this example, it is apparent that columns 1-8are the same as those represented in FIG. 1. The remaining columns ofFIG. 2 identify the buckets, in ascending or descending order (e.g.,(−∞,2], (2,4], (4,6], . . . etc.).

[0057] In this example there exits one row for each category depicted,wherein the categories depicted are labeled as dread 120, dwrite 125,and iread 130 and one row for the composite 140. The composite row 140can exist as either the first or last row. The categories 115 presentedherein are for example only and are not restricted to the specific typesof categorical data shown by FIG. 2. The sample count 145 data for eachbucket column 150 is presented as an integer 145 in its correspondingcell. The calculated percentage of the data samples in each bucketcolumn 150 is represented as a level of a shade of gray 110. This is butone of the advantages of the method disclosed, wherein the statisticalinformation can be displayed in gray-scale 110. Such use of gray-scale110 allows for greater clarity in displaying various statistical data.

[0058] The effect of combining histogram data with standard statisticalmeasures provides a complete compact view of the data, which is easy togenerate and can be viewed by HTML browsers, and viewed and manipulatedin spreadsheets. Further, the data is structured using HTML tables tofacilitate easy interpretation by Microsoft Excels or other applicationsbut is not limited by these examples shown herein. Such a compact viewis very difficult to achieve when histogram data is presented usingcharts. In addition, the method of presenting all of the sample counts145 in a single table allows data to be manipulated and graphed easier.For example, Graph 2 below was easily generated from FIG. 2 usingMicrosoft Excel.

[0059] Referring now to FIG. 3, a flowchart is shown which depicts themethod steps disclosed herein by the present invention. The method isperformed by a computer having at least one display, wherein thecomputer comprises a central processing unit programmed with a computerprogram product and its code comprising the method herein, wherein thecomputer is communicably coupled to at least one display for displayingtabular data and superimposing gray-scale percentage information, orother statistical information, over individual statistic cell count dataall within a single HTML based table. The specific algorithm used by themethod of the present invention is disclosed and depicted in FIG. 4.

[0060] In more specific reference now to FIG. 3, the method ofsuperimposing gray-scale percentage information over individual cellstatistical data described above begins with the step of collecting aset of statistic samples 310. Step 310 further comprises grouping thecollected set of statistic samples according to their category andassigning them to a set of buckets. Although step 310 is outside thescope of the invention disclosed herein it is provided for clarity,continuity, and completeness of the method. Next, step 315 then countsthe total number of samples collected in step 310.

[0061] Further, step 320 outputs a table header comprising a column forthe category and a column for each bucket which specifies therein itsbounds (e.g., (−∞,2], (2,4], (4,6], etc.). Step 320 also comprisesincluding columns for other standard statistical measures (e.g., Mean,Min, Max, Std. Dev., etc. as desired).

[0062] Next, in step 330, the method then determines if the process isdone with the categories. If the process is done with the categories 335then the end of the table is marked in step 340 and is ended 345. If, asin step 330, the process is not done with the categories 350, then as instep 355 a new row is started. In step 355 each new row started beginswith cells for the category name and standard measures for the currentcategory.

[0063] Next, step 365 determines if the process is done with thebuckets. If the process is done with the buckets 370, the end of the rowis marked in step 375 and repeats with step 330 as explained in theprevious paragraph. If, as in step 380, the process is not done with thebuckets the step of calculating the fraction of samples 385 with thecurrent category in the current bucket is accomplished.

[0064] Next, step 390 requires converting the fraction calculated instep 385 to a shade of gray. This conversion is accomplished bysubtracting the fraction derived in step 385 from one, multiplying theresult by 256 (colors), converting the result to a pair of hexadecimaldigits, and using the resulting hexadecimal digits for the red, green,and blue components of an HTML color.

[0065] Following the conversion to a shade of gray as explained above,step 395 outputs a cell for the current category and current bucket.This is done using the color computed in step 390 as the backgroundcolor. The foreground (text color) is white if the fraction is less than0.6 and black otherwise, in order to improve contrast with the cellbackground. Following step 395 the process repeats step 365 as explainedabove.

[0066] It should be emphasized that the above-described methods of thepresent invention, particularly, any “preferred” embodiments, are merelypossible examples of implementations, merely set forth for a clearunderstanding of the principles of the invention. Many variations andmodifications will be apparent to persons skilled in the art uponreference to the description and may be made to the above-describedembodiment(s) of the invention without departing substantially from thespirit and principles of the invention. All such modifications andvariations are intended to be included herein within the scope of thisdisclosure and the present invention and protected by the followingclaims.

1. A method for compact tabular display of statistical information, themethod comprising: a means for displaying statistical data in tabularform; and, a means for superimposing statistical information over thedisplayed statistical data using a plurality of shades of gray.
 2. Themethod of claim 1, wherein the statistical information comprises samplecount data.
 3. The method of claim 2, wherein the statisticalinformation further comprises sample count percentage values.
 4. Themethod of claim 1, wherein the plurality of shades of gray includedarker shades of gray, wherein the darker shades of gray representlarger percentage values.
 5. The method of claim 1, wherein theplurality of shades of gray include lighter shades of gray, wherein thelighter shades of gray represent smaller percentage values.
 6. Themethod of claim 4, wherein superimposed percentage values having darkershades of gray, the accompanying cell data is displayed in white textcolor.
 7. A method for enabling comprehensive and simultaneous displayof a plurality of statistical data presented within a single compacttable, the method comprising: a. counting the total number of samples;b. outputting a table header, c. determining if the categories arecomplete, wherein if the categories are complete the table isterminated, wherein if the categories are not complete then the methodcontinues with step (d); d. starting a row; e. determining if thebuckets are complete, wherein if the buckets are complete the row isterminated and step (c) is repeated, wherein if the buckets are notcomplete then the method continues with step (f); f. calculating thefraction of samples; g. converting the fraction to a shade of gray; and,h. outputting a cell for the current category and bucket.
 8. The methodas in claim 7, wherein the table header of step (b) comprises aplurality of columns for a plurality of standard statistical measurescomprising columns for the category and for each bucket, wherein thebucket category specifies each bucket's range bounds.
 9. The method asin claim 8, wherein the table header of step (b) further includescolumns for displaying a plurality of statistical measures selected fromthe group consisting of context, count, minimum, mean, maximum, standarddeviation, and coefficient of variation.
 10. The method of claim 7,wherein step (d) the row begins with cells for the category name andstandard measures for the current category.
 11. The method of claim 7,wherein step (f) the fraction of samples with the current category inthe current bucket are calculated.
 12. The method of claim 7, whereinstep (g) is accomplished by subtracting the fraction in step (f) fromone, multiplying the result by 256, and converting the result to a pairof hexadecimal digits.
 13. The method of claim 12, wherein the resultinghexadecimal digits are used for the red, green, and blue components ofan HTML color.
 14. The method of claim 7, wherein the output cell ofstep (h) uses the color computed in step (g) as its background color.15. The method of claim 14, wherein the foreground color is white if thefraction calculated in step (f) is greater than 0.6.
 16. The method ofclaim 15, wherein after step (h) is complete, step (e) is repeated. 17.A method for enabling a plurality of sample count and percentageinformation data to be displayed within a single compact table, themethod comprising: a. calculating data percentage figures; b.determining a gray-scale shade to apply to a data cell; c. applying abackground color to a data cell; d. determining the foreground color ofa data cell; and e. applying the foreground color to a data cell.
 18. Amethod for presenting a plurality of tabular data, wherein the datacomprises raw data counts and percentage information, the methodcomprising: a. grouping statistical sample count data; b. creating astatistical table; c. displaying statistical count data in tabular form;d. calculating percentage information, wherein the percentageinformation relates to the count data within a specific data cell ascompared to the total count; e. concurrently presenting sample countdata information and calculated percentage information in the createdtable, wherein the percentage information is displayed and presentedusing shades of gray, thereby allowing a plurality of data andcalculated percentage information to be displayed and viewed in a singlecompact tabular format.
 19. A computer program product comprisingcomputer program code for simultaneously displaying statisticalinformation within a single table, the computer program code comprising:a computer program code portion for creating a statistical table,wherein the table has a plurality of columns and rows; a computerprogram code portion for displaying statistical data within the table; acomputer program code portion for calculating percentage informationrelating to the statistical data; and a computer program code portioncapable of superimposing and displaying the calculated percentageinformation relating to the data in a plurality of shades of gray. 20.The computer program product of claim 19, wherein the statisticalinformation comprises sample count data.
 21. The computer programproduct of claim 20, wherein the statistical information furthercomprises sample count percentage values.
 22. The computer programproduct of claim 19, wherein the plurality of shades of gray includedarker shades of gray, wherein the darker shades of gray representlarger percentage values.
 23. The computer program product of claim 19,wherein the plurality of shades of gray include lighter shades of gray,wherein the lighter shades of gray represent smaller percentage values.24. The computer program product of claim 22, wherein superimposedpercentage values having darker shades of gray, the accompanying celldata is displayed in white text color.
 25. A computer program capable ofexecuting a method of calculating and superimposing varying shades ofgray over cell data, wherein the shade of gray represents statisticaldata percentage information, the method comprising: providing a signalfor creating a statistical table; and, simultaneously displayingstatistical data information having related percentage valuecalculations, wherein the values are approximately indicated bysuperimposing shades of gray selected by the program from a gray-scalecolor palette over a plurality of cell data within the statistical datatable, thereby allowing an analyst to easily interpret a plurality ofstatistical information comprised within a compact single table.
 26. Acomputer for processing and displaying statistical information intabular form, the computer comprising: a central processing unit; aleast one display communicably coupled to the central processing unit; aleast one central processing unit program, wherein the program isoperative to provide a signal for creating a statistical table; and,wherein the display communicates with the central processing unit tosimultaneously display statistical data information having relatedpercentage value calculations, wherein the values are approximatelyindicated by superimposing shades of gray selected by the program from agray-scale color palette over a plurality of cell data within thestatistical data table, thereby allowing an analyst to easily interpreta plurality of statistical information comprised within a compact singletable.
 27. The computer of claim 26, wherein the statistical informationcomprises sample count data.
 28. The computer of claim 27, wherein thestatistical information further comprises sample count percentagevalues.
 29. The computer of claim 26, wherein the palette comprises aplurality of shades of gray, wherein the palette includes darker shadesof gray, wherein the darker shades of gray represent larger percentagevalues.
 30. The computer of claim 26, wherein the palette comprises aplurality of shades of gray, wherein the palette includes lighter shadesof gray, wherein the lighter shades of gray represent smaller percentagevalues.
 31. The computer of claim 29, wherein superimposed percentagevalues having darker shades of gray, the accompanying cell data isdisplayed in white text color.